Statistical modelling for zoonotic diseases : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Statistics at Massey University, Palmerston North, New Zealand
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Date
2020
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Massey University
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Abstract
Preventing and controlling zoonoses through the design and implementation of public health policies requires a thorough understanding of epidemiology and transmission pathways. A pathogen may have complex transmission pathways that could be affected by environmental factors, different reservoirs and the food chain. One way to get more insight into a zoonosis is to trace back the putative sources of infection. Approaches to attribute the infection to sources include epidemiological observations and microbial subtyping techniques. In order for source attribution from the pathways to human infection to be delineated, this thesis proposes statistical modelling methods with an integration of demographic variables with multilocus sequence typing data derived from human cases and sources. These models are framed in a Bayesian context, allowing for a flexible use of limited knowledge about the illness to make inferences about the potential sources contributing to human infection.
These methods are applied to campylobacteriosis data collected from a surveillance sentinel site in the Manawatu region of New Zealand. A link between genotypes found from sources and human samples is considered in the modelling scheme, assuming
genotypes from sources are equal or linked indirectly to that from human cases. Model diagnostics show that the assumption of equal prevalence of genotypes between humans and sources is not tenable, with a few types being potentially more prevalent in humans than in sources, or vice versa. Thus, a model that allows genotypes on humans to differ from those on sources is implemented. In addition, an approximate Bayesian model is also proposed, which essentially cuts the link between human and source genotype distributions when conducting inference.
The final inference from these approaches is the probability for human cases attributable to each source, conditional on the extent to which each case resides in a rural compared to urban environment. Results from the effective models suggest that
poultry and ruminants are important sources for human campylobacteriosis. The more rural human cases are located, the higher the likelihood of ruminant-sourced cases is. In contrast, cases are more poultry-associated when their locations are more urban. A
little rurality effect is noticed for water and other sources due to small sample sizes compared to that from poultry and ruminants. In addition, animal faeces are believed to be the primary cause of water contamination via rainfall or runoff coming from
farmland and pasture. When water is treated as a medium in the transmission, instead of an end point, water birds are suggested to be the most likely contributor to water contamination.
These findings have implications for public health practice and food safety risk management. A risk management strategy had been carried out in the poultry industry in New Zealand, leading to a marked decrease of urban case rates from a poultry source.
However, the findings of this thesis suggest a further step with a focus on rural areas as rural case rates are observed to be relatively higher than urban rates. Further, by exploring the role that water plays in the transmission, it deepens our knowledge of
the epidemiology about waterborne campylobacteriosis and highlights the importance of water quality. This opens a potential research direction to study the association of water quality and environmental factors such as higher global temperatures for this
disease.
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Figure 2.1 is published with permission from the American Society for Microbiology.
Figure 2.1 is published with permission from the American Society for Microbiology.
Keywords
Zoonoses, Mathematical models, Statistical methods, Campylobacter infections, New Zealand, Manawatu-Wanganui, Epidemiology